Overview

Dataset statistics

Number of variables28
Number of observations4816
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory224.0 B

Variable types

Text3
Numeric10
Categorical15

Alerts

Score is highly overall correlated with Real Guest Cleanlines Score and 5 other fieldsHigh correlation
Reviews is highly overall correlated with Booked todayHigh correlation
Booked today is highly overall correlated with ReviewsHigh correlation
Real Guest Cleanlines Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Facilities Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Location Score is highly overall correlated with Score and 4 other fieldsHigh correlation
Real Guest Service Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Real Guest Value for money Score is highly overall correlated with Score and 5 other fieldsHigh correlation
Sparkling clean is highly overall correlated with Score and 4 other fieldsHigh correlation
NewlyBuilt is highly imbalanced (85.2%)Imbalance
ExcellentView is highly imbalanced (72.9%)Imbalance
Free WiFi In All Rooms is highly imbalanced (69.9%)Imbalance
Kids club is highly imbalanced (67.2%)Imbalance
Stars has 552 (11.5%) zerosZeros
Booked today has 3435 (71.3%) zerosZeros

Reproduction

Analysis started2023-06-15 16:27:40.633055
Analysis finished2023-06-15 16:28:10.458634
Duration29.83 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Name
Text

Distinct4289
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:11.035090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length100
Median length65
Mean length25.557724
Min length3

Characters and Unicode

Total characters123086
Distinct characters257
Distinct categories14 ?
Distinct scripts7 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3801 ?
Unique (%)78.9%

Sample

1st rowAsia Hotel Bangkok (SHA Plus+)
2nd rowRembrandt Hotel & Suites (SHA Plus+)
3rd rowDream Hotel Bangkok (SHA Plus+)
4th rowVIX Bangkok @ Victory Monument
5th rowThe Berkeley Hotel Pratunam (SHA Plus+)
ValueCountFrequency (%)
hotel 1293
 
6.4%
sha 1123
 
5.6%
plus 1033
 
5.1%
resort 621
 
3.1%
bangkok 528
 
2.6%
phuket 488
 
2.4%
the 471
 
2.3%
extra 466
 
2.3%
331
 
1.6%
hostel 305
 
1.5%
Other values (3802) 13433
66.9%
2023-06-15T19:28:11.848262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15276
 
12.4%
e 9665
 
7.9%
a 9287
 
7.5%
o 7148
 
5.8%
t 6909
 
5.6%
n 5658
 
4.6%
l 5297
 
4.3%
i 4600
 
3.7%
s 4553
 
3.7%
u 4350
 
3.5%
Other values (247) 50343
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 78980
64.2%
Uppercase Letter 23373
 
19.0%
Space Separator 15276
 
12.4%
Decimal Number 1338
 
1.1%
Close Punctuation 1140
 
0.9%
Open Punctuation 1138
 
0.9%
Math Symbol 555
 
0.5%
Other Punctuation 509
 
0.4%
Other Letter 468
 
0.4%
Dash Punctuation 208
 
0.2%
Other values (4) 101
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
า 30
 
6.4%
น 29
 
6.2%
อ 24
 
5.1%
ร 23
 
4.9%
เ 22
 
4.7%
ล 15
 
3.2%
ง 15
 
3.2%
ม 14
 
3.0%
บ 13
 
2.8%
ส 13
 
2.8%
Other values (111) 270
57.7%
Lowercase Letter
ValueCountFrequency (%)
e 9665
12.2%
a 9287
11.8%
o 7148
9.1%
t 6909
8.7%
n 5658
 
7.2%
l 5297
 
6.7%
i 4600
 
5.8%
s 4553
 
5.8%
u 4350
 
5.5%
r 4280
 
5.4%
Other values (46) 17233
21.8%
Uppercase Letter
ValueCountFrequency (%)
H 3483
14.9%
P 2938
12.6%
S 2781
11.9%
A 2084
8.9%
B 1941
 
8.3%
R 1561
 
6.7%
T 1153
 
4.9%
E 884
 
3.8%
C 843
 
3.6%
M 666
 
2.8%
Other values (20) 5039
21.6%
Other Punctuation
ValueCountFrequency (%)
& 187
36.7%
@ 114
22.4%
. 69
 
13.6%
' 58
 
11.4%
, 46
 
9.0%
/ 10
 
2.0%
! 8
 
1.6%
: 6
 
1.2%
• 6
 
1.2%
# 3
 
0.6%
Other values (2) 2
 
0.4%
Nonspacing Mark
ValueCountFrequency (%)
ิ 18
20.7%
้ 17
19.5%
์ 14
16.1%
่ 11
12.6%
ี 9
10.3%
ั 5
 
5.7%
ุ 4
 
4.6%
ื 4
 
4.6%
ู 2
 
2.3%
็ 1
 
1.1%
Other values (2) 2
 
2.3%
Decimal Number
ValueCountFrequency (%)
1 264
19.7%
2 235
17.6%
3 141
10.5%
4 141
10.5%
8 113
8.4%
5 110
8.2%
9 94
 
7.0%
7 85
 
6.4%
0 84
 
6.3%
6 71
 
5.3%
Close Punctuation
ValueCountFrequency (%)
) 1126
98.8%
] 9
 
0.8%
】 3
 
0.3%
） 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1124
98.8%
[ 9
 
0.8%
【 3
 
0.3%
（ 2
 
0.2%
Math Symbol
ValueCountFrequency (%)
+ 554
99.8%
~ 1
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 204
98.1%
– 4
 
1.9%
Space Separator
ValueCountFrequency (%)
15276
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 5
100.0%
Other Symbol
ValueCountFrequency (%)
❤ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 102209
83.0%
Common 20178
 
16.4%
Thai 421
 
0.3%
Cyrillic 144
 
0.1%
Han 114
 
0.1%
Arabic 19
 
< 0.1%
Inherited 1
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
店 6
 
5.3%
酒 6
 
5.3%
东 4
 
3.5%
寓 4
 
3.5%
公 4
 
3.5%
海 3
 
2.6%
吉 3
 
2.6%
普 3
 
2.6%
岛 2
 
1.8%
谷 2
 
1.8%
Other values (64) 77
67.5%
Latin
ValueCountFrequency (%)
e 9665
 
9.5%
a 9287
 
9.1%
o 7148
 
7.0%
t 6909
 
6.8%
n 5658
 
5.5%
l 5297
 
5.2%
i 4600
 
4.5%
s 4553
 
4.5%
u 4350
 
4.3%
r 4280
 
4.2%
Other values (47) 40462
39.6%
Thai
ValueCountFrequency (%)
า 30
 
7.1%
น 29
 
6.9%
อ 24
 
5.7%
ร 23
 
5.5%
เ 22
 
5.2%
ิ 18
 
4.3%
้ 17
 
4.0%
ล 15
 
3.6%
ง 15
 
3.6%
์ 14
 
3.3%
Other values (35) 214
50.8%
Common
ValueCountFrequency (%)
15276
75.7%
) 1126
 
5.6%
( 1124
 
5.6%
+ 554
 
2.7%
1 264
 
1.3%
2 235
 
1.2%
- 204
 
1.0%
& 187
 
0.9%
3 141
 
0.7%
4 141
 
0.7%
Other values (28) 926
 
4.6%
Cyrillic
ValueCountFrequency (%)
а 20
13.9%
н 13
 
9.0%
е 12
 
8.3%
о 9
 
6.2%
т 9
 
6.2%
л 8
 
5.6%
в 8
 
5.6%
п 7
 
4.9%
ы 6
 
4.2%
м 6
 
4.2%
Other values (19) 46
31.9%
Arabic
ValueCountFrequency (%)
ا 4
21.1%
ل 2
10.5%
ع 2
10.5%
ر 2
10.5%
ن 1
 
5.3%
ف 1
 
5.3%
د 1
 
5.3%
ق 1
 
5.3%
م 1
 
5.3%
س 1
 
5.3%
Other values (3) 3
15.8%
Inherited
ValueCountFrequency (%)
️ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122352
99.4%
Thai 421
 
0.3%
Cyrillic 144
 
0.1%
CJK 114
 
0.1%
None 19
 
< 0.1%
Arabic 19
 
< 0.1%
Punctuation 15
 
< 0.1%
VS 1
 
< 0.1%
Dingbats 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15276
 
12.5%
e 9665
 
7.9%
a 9287
 
7.6%
o 7148
 
5.8%
t 6909
 
5.6%
n 5658
 
4.6%
l 5297
 
4.3%
i 4600
 
3.8%
s 4553
 
3.7%
u 4350
 
3.6%
Other values (71) 49609
40.5%
Thai
ValueCountFrequency (%)
า 30
 
7.1%
น 29
 
6.9%
อ 24
 
5.7%
ร 23
 
5.5%
เ 22
 
5.2%
ิ 18
 
4.3%
้ 17
 
4.0%
ล 15
 
3.6%
ง 15
 
3.6%
์ 14
 
3.3%
Other values (35) 214
50.8%
Cyrillic
ValueCountFrequency (%)
а 20
13.9%
н 13
 
9.0%
е 12
 
8.3%
о 9
 
6.2%
т 9
 
6.2%
л 8
 
5.6%
в 8
 
5.6%
п 7
 
4.9%
ы 6
 
4.2%
м 6
 
4.2%
Other values (19) 46
31.9%
CJK
ValueCountFrequency (%)
店 6
 
5.3%
酒 6
 
5.3%
东 4
 
3.5%
寓 4
 
3.5%
公 4
 
3.5%
海 3
 
2.6%
吉 3
 
2.6%
普 3
 
2.6%
岛 2
 
1.8%
谷 2
 
1.8%
Other values (64) 77
67.5%
Punctuation
ValueCountFrequency (%)
• 6
40.0%
’ 5
33.3%
– 4
26.7%
None
ValueCountFrequency (%)
ö 4
21.1%
】 3
15.8%
【 3
15.8%
） 2
10.5%
（ 2
10.5%
à 1
 
5.3%
é 1
 
5.3%
â 1
 
5.3%
1
 
5.3%
è 1
 
5.3%
Arabic
ValueCountFrequency (%)
ا 4
21.1%
ل 2
10.5%
ع 2
10.5%
ر 2
10.5%
ن 1
 
5.3%
ف 1
 
5.3%
د 1
 
5.3%
ق 1
 
5.3%
م 1
 
5.3%
س 1
 
5.3%
Other values (3) 3
15.8%
VS
ValueCountFrequency (%)
️ 1
100.0%
Dingbats
ValueCountFrequency (%)
❤ 1
100.0%

Price
Real number (ℝ)

Distinct531
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.08783
Minimum12
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:12.140362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile46
Q169
median96
Q3156
95-th percentile482.25
Maximum999
Range987
Interquartile range (IQR)87

Descriptive statistics

Standard deviation147.10363
Coefficient of variation (CV)0.99335395
Kurtosis9.1265146
Mean148.08783
Median Absolute Deviation (MAD)34
Skewness2.8565783
Sum713191
Variance21639.479
MonotonicityNot monotonic
2023-06-15T19:28:12.459510image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
79 116
 
2.4%
69 104
 
2.2%
99 87
 
1.8%
89 87
 
1.8%
59 69
 
1.4%
49 67
 
1.4%
72 63
 
1.3%
73 60
 
1.2%
66 58
 
1.2%
85 57
 
1.2%
Other values (521) 4048
84.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
19 1
 
< 0.1%
21 3
0.1%
22 2
 
< 0.1%
24 2
 
< 0.1%
25 1
 
< 0.1%
27 5
0.1%
28 2
 
< 0.1%
29 3
0.1%
30 6
0.1%
ValueCountFrequency (%)
999 1
< 0.1%
995 1
< 0.1%
987 1
< 0.1%
975 1
< 0.1%
968 2
< 0.1%
967 1
< 0.1%
965 1
< 0.1%
964 1
< 0.1%
959 1
< 0.1%
957 2
< 0.1%

Stars
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8636836
Minimum0
Maximum5
Zeros552
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:12.671939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3539495
Coefficient of variation (CV)0.47279998
Kurtosis0.047239375
Mean2.8636836
Median Absolute Deviation (MAD)1
Skewness-0.66597306
Sum13791.5
Variance1.8331793
MonotonicityNot monotonic
2023-06-15T19:28:12.834506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 1545
32.1%
4 819
17.0%
2 681
14.1%
0 552
 
11.5%
5 400
 
8.3%
2.5 283
 
5.9%
3.5 258
 
5.4%
4.5 172
 
3.6%
1.5 56
 
1.2%
1 50
 
1.0%
ValueCountFrequency (%)
0 552
 
11.5%
1 50
 
1.0%
1.5 56
 
1.2%
2 681
14.1%
2.5 283
 
5.9%
3 1545
32.1%
3.5 258
 
5.4%
4 819
17.0%
4.5 172
 
3.6%
5 400
 
8.3%
ValueCountFrequency (%)
5 400
 
8.3%
4.5 172
 
3.6%
4 819
17.0%
3.5 258
 
5.4%
3 1545
32.1%
2.5 283
 
5.9%
2 681
14.1%
1.5 56
 
1.2%
1 50
 
1.0%
0 552
 
11.5%

Score
Real number (ℝ)

Distinct69
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1336171
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:13.020007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.5
Q17.7
median8.3
Q38.7
95-th percentile9.4
Maximum10
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.94040792
Coefficient of variation (CV)0.11561989
Kurtosis5.2385103
Mean8.1336171
Median Absolute Deviation (MAD)0.5
Skewness-1.4547623
Sum39171.5
Variance0.88436706
MonotonicityNot monotonic
2023-06-15T19:28:13.242934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4 304
 
6.3%
8.5 256
 
5.3%
8.3 240
 
5.0%
8.7 231
 
4.8%
8.1 230
 
4.8%
8.6 230
 
4.8%
8.8 230
 
4.8%
7.9 223
 
4.6%
8.2 223
 
4.6%
8 211
 
4.4%
Other values (59) 2438
50.6%
ValueCountFrequency (%)
2 2
 
< 0.1%
2.3 5
0.1%
2.5 1
 
< 0.1%
2.8 2
 
< 0.1%
3 2
 
< 0.1%
3.1 4
0.1%
3.3 1
 
< 0.1%
3.5 1
 
< 0.1%
3.6 4
0.1%
3.9 1
 
< 0.1%
ValueCountFrequency (%)
10 54
1.1%
9.9 11
 
0.2%
9.8 32
 
0.7%
9.7 16
 
0.3%
9.6 50
 
1.0%
9.5 51
1.1%
9.4 53
1.1%
9.3 81
1.7%
9.2 127
2.6%
9.1 126
2.6%

SpokenLanguages
Text

Distinct470
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:13.600979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length389
Median length13
Mean length21.531354
Min length4

Characters and Unicode

Total characters103695
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique293 ?
Unique (%)6.1%

Sample

1st rowEnglish, Thai
2nd rowEnglish, Thai
3rd rowEnglish, Thai
4th rowEnglish, Chinese [Mandarin], Thai
5th rowEnglish, Chinese [Mandarin], Thai
ValueCountFrequency (%)
english 4681
33.3%
thai 4644
33.1%
chinese 1021
 
7.3%
mandarin 888
 
6.3%
french 348
 
2.5%
german 246
 
1.8%
burmese 224
 
1.6%
russian 221
 
1.6%
japanese 218
 
1.6%
hindi 166
 
1.2%
Other values (35) 1392
 
9.9%
2023-06-15T19:28:14.402651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 12970
12.5%
h 11055
10.7%
n 9734
9.4%
9233
8.9%
a 8575
 
8.3%
, 8212
 
7.9%
s 7067
 
6.8%
l 5123
 
4.9%
g 4733
 
4.6%
E 4687
 
4.5%
Other values (36) 22306
21.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 70159
67.7%
Uppercase Letter 14049
 
13.5%
Space Separator 9233
 
8.9%
Other Punctuation 8212
 
7.9%
Open Punctuation 1021
 
1.0%
Close Punctuation 1021
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 12970
18.5%
h 11055
15.8%
n 9734
13.9%
a 8575
12.2%
s 7067
10.1%
l 5123
 
7.3%
g 4733
 
6.7%
e 4208
 
6.0%
r 2011
 
2.9%
d 1135
 
1.6%
Other values (12) 3548
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
E 4687
33.4%
T 4685
33.3%
C 1173
 
8.3%
M 960
 
6.8%
F 521
 
3.7%
G 254
 
1.8%
B 231
 
1.6%
R 223
 
1.6%
J 218
 
1.6%
H 186
 
1.3%
Other values (10) 911
 
6.5%
Space Separator
ValueCountFrequency (%)
9233
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8212
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 1021
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1021
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 84208
81.2%
Common 19487
 
18.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 12970
15.4%
h 11055
13.1%
n 9734
11.6%
a 8575
10.2%
s 7067
8.4%
l 5123
 
6.1%
g 4733
 
5.6%
E 4687
 
5.6%
T 4685
 
5.6%
e 4208
 
5.0%
Other values (32) 11371
13.5%
Common
ValueCountFrequency (%)
9233
47.4%
, 8212
42.1%
[ 1021
 
5.2%
] 1021
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103695
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 12970
12.5%
h 11055
10.7%
n 9734
9.4%
9233
8.9%
a 8575
 
8.3%
, 8212
 
7.9%
s 7067
 
6.8%
l 5123
 
4.9%
g 4733
 
4.6%
E 4687
 
4.5%
Other values (36) 22306
21.5%

Reviews
Real number (ℝ)

Distinct1737
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean974.69934
Minimum1
Maximum61617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:14.762687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q158.75
median269
Q3992.25
95-th percentile4050.25
Maximum61617
Range61616
Interquartile range (IQR)933.5

Descriptive statistics

Standard deviation2197.4298
Coefficient of variation (CV)2.2544694
Kurtosis157.83596
Mean974.69934
Median Absolute Deviation (MAD)252
Skewness8.9297466
Sum4694152
Variance4828697.6
MonotonicityNot monotonic
2023-06-15T19:28:15.006359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
2.1%
2 86
 
1.8%
3 55
 
1.1%
5 48
 
1.0%
4 41
 
0.9%
8 36
 
0.7%
9 34
 
0.7%
6 30
 
0.6%
22 29
 
0.6%
10 28
 
0.6%
Other values (1727) 4330
89.9%
ValueCountFrequency (%)
1 99
2.1%
2 86
1.8%
3 55
1.1%
4 41
0.9%
5 48
1.0%
6 30
 
0.6%
7 24
 
0.5%
8 36
 
0.7%
9 34
 
0.7%
10 28
 
0.6%
ValueCountFrequency (%)
61617 1
< 0.1%
28839 1
< 0.1%
28073 1
< 0.1%
27771 2
< 0.1%
25454 1
< 0.1%
24128 1
< 0.1%
23462 1
< 0.1%
22016 1
< 0.1%
20544 1
< 0.1%
18583 1
< 0.1%

Location
Text

Distinct3802
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:15.705943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length236
Median length135
Mean length71.869186
Min length22

Characters and Unicode

Total characters346122
Distinct characters153
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3304 ?
Unique (%)68.6%

Sample

1st row296 Phayathai Road, Siam, Bangkok, Thailand, 10400
2nd row19 Sukhumvit Soi 18, Klong Toei, Sukhumvit, Bangkok, Thailand, 10110
3rd row10 Sukhumvit Soi 15, Sukhumvit, Bangkok, Thailand, 10110
4th row13-15 Thanon Ratchawithi, Chatuchak, Bangkok, Thailand, 10400
5th row559 Ratchathewi, Pratunam, Bangkok, Thailand, 10400
ValueCountFrequency (%)
thailand 5024
 
10.3%
bangkok 2990
 
6.1%
phuket 2896
 
5.9%
road 1632
 
3.3%
soi 1466
 
3.0%
sukhumvit 1018
 
2.1%
patong 801
 
1.6%
rd 683
 
1.4%
83150 672
 
1.4%
moo 596
 
1.2%
Other values (5547) 30985
63.5%
2023-06-15T19:28:16.948468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43947
 
12.7%
a 36935
 
10.7%
, 24047
 
6.9%
n 20240
 
5.8%
h 16868
 
4.9%
o 16311
 
4.7%
i 14300
 
4.1%
k 12319
 
3.6%
t 11726
 
3.4%
0 10247
 
3.0%
Other values (143) 139182
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 191131
55.2%
Space Separator 43947
 
12.7%
Decimal Number 41119
 
11.9%
Uppercase Letter 37713
 
10.9%
Other Punctuation 27615
 
8.0%
Other Letter 2502
 
0.7%
Dash Punctuation 1237
 
0.4%
Nonspacing Mark 404
 
0.1%
Open Punctuation 220
 
0.1%
Close Punctuation 219
 
0.1%
Other values (4) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
น 221
 
8.8%
า 213
 
8.5%
อ 175
 
7.0%
ร 164
 
6.6%
ง 147
 
5.9%
เ 116
 
4.6%
ก 109
 
4.4%
ม 105
 
4.2%
ย 94
 
3.8%
ต 91
 
3.6%
Other values (47) 1067
42.6%
Uppercase Letter
ValueCountFrequency (%)
T 7407
19.6%
P 6270
16.6%
S 4504
11.9%
B 4435
11.8%
R 3777
10.0%
K 2931
 
7.8%
M 1761
 
4.7%
N 1155
 
3.1%
A 1142
 
3.0%
C 950
 
2.5%
Other values (17) 3381
9.0%
Lowercase Letter
ValueCountFrequency (%)
a 36935
19.3%
n 20240
10.6%
h 16868
8.8%
o 16311
8.5%
i 14300
 
7.5%
k 12319
 
6.4%
t 11726
 
6.1%
u 9042
 
4.7%
d 8849
 
4.6%
g 8375
 
4.4%
Other values (16) 36166
18.9%
Decimal Number
ValueCountFrequency (%)
0 10247
24.9%
1 9791
23.8%
3 4413
10.7%
8 3751
 
9.1%
2 3726
 
9.1%
5 2582
 
6.3%
4 2257
 
5.5%
6 1578
 
3.8%
9 1470
 
3.6%
7 1303
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 24047
87.1%
/ 2359
 
8.5%
. 1183
 
4.3%
& 12
 
< 0.1%
; 4
 
< 0.1%
: 3
 
< 0.1%
# 3
 
< 0.1%
@ 2
 
< 0.1%
* 1
 
< 0.1%
، 1
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
ิ 59
14.6%
ั 54
13.4%
ุ 50
12.4%
่ 46
11.4%
้ 45
11.1%
ู 42
10.4%
ี 40
9.9%
์ 32
7.9%
ื 23
 
5.7%
็ 13
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 1235
99.8%
– 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 218
99.1%
[ 2
 
0.9%
Close Punctuation
ValueCountFrequency (%)
) 217
99.1%
] 2
 
0.9%
Math Symbol
ValueCountFrequency (%)
+ 6
60.0%
| 4
40.0%
Space Separator
ValueCountFrequency (%)
43947
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 2
100.0%
Currency Symbol
ValueCountFrequency (%)
€ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 228844
66.1%
Common 114371
33.0%
Thai 2899
 
0.8%
Arabic 8
 
< 0.1%

Most frequent character per script

Thai
ValueCountFrequency (%)
น 221
 
7.6%
า 213
 
7.3%
อ 175
 
6.0%
ร 164
 
5.7%
ง 147
 
5.1%
เ 116
 
4.0%
ก 109
 
3.8%
ม 105
 
3.6%
ย 94
 
3.2%
ต 91
 
3.1%
Other values (50) 1464
50.5%
Latin
ValueCountFrequency (%)
a 36935
16.1%
n 20240
 
8.8%
h 16868
 
7.4%
o 16311
 
7.1%
i 14300
 
6.2%
k 12319
 
5.4%
t 11726
 
5.1%
u 9042
 
4.0%
d 8849
 
3.9%
g 8375
 
3.7%
Other values (43) 73879
32.3%
Common
ValueCountFrequency (%)
43947
38.4%
, 24047
21.0%
0 10247
 
9.0%
1 9791
 
8.6%
3 4413
 
3.9%
8 3751
 
3.3%
2 3726
 
3.3%
5 2582
 
2.3%
/ 2359
 
2.1%
4 2257
 
2.0%
Other values (22) 7251
 
6.3%
Arabic
ValueCountFrequency (%)
ا 1
12.5%
ة 1
12.5%
س 1
12.5%
م 1
12.5%
ق 1
12.5%
د 1
12.5%
ن 1
12.5%
ف 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 343205
99.2%
Thai 2899
 
0.8%
Arabic 9
 
< 0.1%
Punctuation 5
 
< 0.1%
Currency Symbols 2
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43947
 
12.8%
a 36935
 
10.8%
, 24047
 
7.0%
n 20240
 
5.9%
h 16868
 
4.9%
o 16311
 
4.8%
i 14300
 
4.2%
k 12319
 
3.6%
t 11726
 
3.4%
0 10247
 
3.0%
Other values (69) 136265
39.7%
Thai
ValueCountFrequency (%)
น 221
 
7.6%
า 213
 
7.3%
อ 175
 
6.0%
ร 164
 
5.7%
ง 147
 
5.1%
เ 116
 
4.0%
ก 109
 
3.8%
ม 105
 
3.6%
ย 94
 
3.2%
ต 91
 
3.1%
Other values (50) 1464
50.5%
Punctuation
ValueCountFrequency (%)
“ 2
40.0%
– 2
40.0%
’ 1
20.0%
Currency Symbols
ValueCountFrequency (%)
€ 2
100.0%
None
ValueCountFrequency (%)
 2
100.0%
Arabic
ValueCountFrequency (%)
ا 1
11.1%
ة 1
11.1%
س 1
11.1%
م 1
11.1%
ق 1
11.1%
د 1
11.1%
ن 1
11.1%
ف 1
11.1%
، 1
11.1%

Sparkling clean
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
3465 
1
1351 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3465
71.9%
1 1351
 
28.1%

Length

2023-06-15T19:28:17.284930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:17.609062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3465
71.9%
1 1351
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 3465
71.9%
1 1351
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3465
71.9%
1 1351
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3465
71.9%
1 1351
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3465
71.9%
1 1351
 
28.1%

NewlyBuilt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
4714 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4714
97.9%
1 102
 
2.1%

Length

2023-06-15T19:28:17.844434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:18.095306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4714
97.9%
1 102
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 4714
97.9%
1 102
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4714
97.9%
1 102
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4714
97.9%
1 102
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4714
97.9%
1 102
 
2.1%

ExcellentView
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
4592 
1
 
224

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4592
95.3%
1 224
 
4.7%

Length

2023-06-15T19:28:18.240448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:18.404011image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4592
95.3%
1 224
 
4.7%

Most occurring characters

ValueCountFrequency (%)
0 4592
95.3%
1 224
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4592
95.3%
1 224
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4592
95.3%
1 224
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4592
95.3%
1 224
 
4.7%

Check In 24/7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
2959 
1
1857 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2959
61.4%
1 1857
38.6%

Length

2023-06-15T19:28:18.538186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:18.688964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2959
61.4%
1 1857
38.6%

Most occurring characters

ValueCountFrequency (%)
0 2959
61.4%
1 1857
38.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2959
61.4%
1 1857
38.6%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2959
61.4%
1 1857
38.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2959
61.4%
1 1857
38.6%

AirportTransfer
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
2658 
1
2158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2658
55.2%
1 2158
44.8%

Length

2023-06-15T19:28:18.814290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:18.967395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2658
55.2%
1 2158
44.8%

Most occurring characters

ValueCountFrequency (%)
0 2658
55.2%
1 2158
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2658
55.2%
1 2158
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2658
55.2%
1 2158
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2658
55.2%
1 2158
44.8%

Front Desk
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
1
2986 
0
1830 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2986
62.0%
0 1830
38.0%

Length

2023-06-15T19:28:19.101035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:19.247642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2986
62.0%
0 1830
38.0%

Most occurring characters

ValueCountFrequency (%)
1 2986
62.0%
0 1830
38.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2986
62.0%
0 1830
38.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2986
62.0%
0 1830
38.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2986
62.0%
0 1830
38.0%

Valet Parking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
4099 
1
717 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4099
85.1%
1 717
 
14.9%

Length

2023-06-15T19:28:19.364331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:19.542892image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4099
85.1%
1 717
 
14.9%

Most occurring characters

ValueCountFrequency (%)
0 4099
85.1%
1 717
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4099
85.1%
1 717
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4099
85.1%
1 717
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4099
85.1%
1 717
 
14.9%

Free WiFi In All Rooms
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
1
4558 
0
 
258

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4558
94.6%
0 258
 
5.4%

Length

2023-06-15T19:28:19.684045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:19.925912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4558
94.6%
0 258
 
5.4%

Most occurring characters

ValueCountFrequency (%)
1 4558
94.6%
0 258
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4558
94.6%
0 258
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4558
94.6%
0 258
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4558
94.6%
0 258
 
5.4%

Swimming Pool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
3684 
1
1132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3684
76.5%
1 1132
 
23.5%

Length

2023-06-15T19:28:20.327134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:20.477395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3684
76.5%
1 1132
 
23.5%

Most occurring characters

ValueCountFrequency (%)
0 3684
76.5%
1 1132
 
23.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3684
76.5%
1 1132
 
23.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3684
76.5%
1 1132
 
23.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3684
76.5%
1 1132
 
23.5%

Bar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
2695 
1
2121 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2695
56.0%
1 2121
44.0%

Length

2023-06-15T19:28:20.607050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:20.826462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2695
56.0%
1 2121
44.0%

Most occurring characters

ValueCountFrequency (%)
0 2695
56.0%
1 2121
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2695
56.0%
1 2121
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2695
56.0%
1 2121
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2695
56.0%
1 2121
44.0%

Coffee
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
1
2793 
0
2023 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2793
58.0%
0 2023
42.0%

Length

2023-06-15T19:28:21.008973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:21.182508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2793
58.0%
0 2023
42.0%

Most occurring characters

ValueCountFrequency (%)
1 2793
58.0%
0 2023
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2793
58.0%
0 2023
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2793
58.0%
0 2023
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2793
58.0%
0 2023
42.0%

DailyHousekeeping
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
1
3957 
0
859 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3957
82.2%
0 859
 
17.8%

Length

2023-06-15T19:28:21.330115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:21.469741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3957
82.2%
0 859
 
17.8%

Most occurring characters

ValueCountFrequency (%)
1 3957
82.2%
0 859
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3957
82.2%
0 859
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3957
82.2%
0 859
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3957
82.2%
0 859
 
17.8%

Golf
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
4045 
1
771 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4045
84.0%
1 771
 
16.0%

Length

2023-06-15T19:28:21.610382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:21.765967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4045
84.0%
1 771
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 4045
84.0%
1 771
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4045
84.0%
1 771
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4045
84.0%
1 771
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4045
84.0%
1 771
 
16.0%

Kids club
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
0
4526 
1
 
290

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4816
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4526
94.0%
1 290
 
6.0%

Length

2023-06-15T19:28:21.908171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:22.063756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4526
94.0%
1 290
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 4526
94.0%
1 290
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4816
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4526
94.0%
1 290
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4526
94.0%
1 290
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4526
94.0%
1 290
 
6.0%

Booked today
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6056894
Minimum0
Maximum176
Zeros3435
Zeros (%)71.3%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:22.240809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile19
Maximum176
Range176
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.4114466
Coefficient of variation (CV)2.6101657
Kurtosis73.887102
Mean3.6056894
Median Absolute Deviation (MAD)0
Skewness6.4200897
Sum17365
Variance88.575328
MonotonicityNot monotonic
2023-06-15T19:28:22.456232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3435
71.3%
3 189
 
3.9%
4 157
 
3.3%
5 118
 
2.5%
6 108
 
2.2%
7 77
 
1.6%
8 67
 
1.4%
9 62
 
1.3%
10 55
 
1.1%
11 47
 
1.0%
Other values (55) 501
 
10.4%
ValueCountFrequency (%)
0 3435
71.3%
3 189
 
3.9%
4 157
 
3.3%
5 118
 
2.5%
6 108
 
2.2%
7 77
 
1.6%
8 67
 
1.4%
9 62
 
1.3%
10 55
 
1.1%
11 47
 
1.0%
ValueCountFrequency (%)
176 2
< 0.1%
128 1
 
< 0.1%
124 1
 
< 0.1%
109 1
 
< 0.1%
99 1
 
< 0.1%
87 1
 
< 0.1%
82 1
 
< 0.1%
74 1
 
< 0.1%
70 3
0.1%
68 2
< 0.1%

Real Guest Cleanlines Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2638704
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:22.682628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.1
Q17.7
median8.5
Q39
95-th percentile9.8
Maximum10
Range8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.175269
Coefficient of variation (CV)0.14221774
Kurtosis5.197143
Mean8.2638704
Median Absolute Deviation (MAD)0.6
Skewness-1.6833995
Sum39798.8
Variance1.3812572
MonotonicityNot monotonic
2023-06-15T19:28:22.886082image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8 247
 
5.1%
9 224
 
4.7%
8.7 210
 
4.4%
8 202
 
4.2%
8.9 197
 
4.1%
8.2 197
 
4.1%
8.3 196
 
4.1%
8.6 195
 
4.0%
10 194
 
4.0%
8.4 193
 
4.0%
Other values (58) 2761
57.3%
ValueCountFrequency (%)
2 17
0.4%
2.2 2
 
< 0.1%
2.3 2
 
< 0.1%
2.5 14
0.3%
2.7 1
 
< 0.1%
3.3 2
 
< 0.1%
3.8 1
 
< 0.1%
3.9 2
 
< 0.1%
4 17
0.4%
4.2 2
 
< 0.1%
ValueCountFrequency (%)
10 194
4.0%
9.9 22
 
0.5%
9.8 38
 
0.8%
9.7 59
 
1.2%
9.6 86
1.8%
9.5 108
2.2%
9.4 127
2.6%
9.3 149
3.1%
9.2 179
3.7%
9.1 191
4.0%

Real Guest Facilities Score
Real number (ℝ)

Distinct71
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.771387
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:23.092042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.8
Q17.2
median7.9
Q38.5
95-th percentile9.4
Maximum10
Range8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.2039652
Coefficient of variation (CV)0.15492282
Kurtosis3.4412195
Mean7.771387
Median Absolute Deviation (MAD)0.7
Skewness-1.2255495
Sum37427
Variance1.4495321
MonotonicityNot monotonic
2023-06-15T19:28:23.277255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 232
 
4.8%
8.2 223
 
4.6%
8.3 200
 
4.2%
8.1 199
 
4.1%
7.5 194
 
4.0%
7.8 189
 
3.9%
8.5 185
 
3.8%
7.9 178
 
3.7%
8.4 176
 
3.7%
7.7 164
 
3.4%
Other values (61) 2876
59.7%
ValueCountFrequency (%)
2 18
0.4%
2.5 18
0.4%
2.7 2
 
< 0.1%
3 4
 
0.1%
3.3 8
0.2%
3.4 2
 
< 0.1%
3.5 3
 
0.1%
3.6 1
 
< 0.1%
3.7 1
 
< 0.1%
3.8 1
 
< 0.1%
ValueCountFrequency (%)
10 143
3.0%
9.9 1
 
< 0.1%
9.8 7
 
0.1%
9.7 17
 
0.4%
9.6 24
 
0.5%
9.5 41
 
0.9%
9.4 44
 
0.9%
9.3 72
1.5%
9.2 65
1.3%
9.1 70
1.5%

Real Guest Location Score
Real number (ℝ)

Distinct67
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1270764
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:23.486694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.4
Q17.6
median8.3
Q38.8
95-th percentile9.5
Maximum10
Range8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.066504
Coefficient of variation (CV)0.13122849
Kurtosis5.8801373
Mean8.1270764
Median Absolute Deviation (MAD)0.6
Skewness-1.6156706
Sum39140
Variance1.1374308
MonotonicityNot monotonic
2023-06-15T19:28:23.678239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 269
 
5.6%
8.4 240
 
5.0%
8.3 228
 
4.7%
8.8 223
 
4.6%
8.5 218
 
4.5%
8.6 210
 
4.4%
8.7 205
 
4.3%
8.2 197
 
4.1%
8.1 197
 
4.1%
8.9 193
 
4.0%
Other values (57) 2636
54.7%
ValueCountFrequency (%)
2 12
0.2%
2.5 14
0.3%
2.7 1
 
< 0.1%
3 6
0.1%
3.2 1
 
< 0.1%
3.4 1
 
< 0.1%
3.5 3
 
0.1%
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4 9
0.2%
ValueCountFrequency (%)
10 141
2.9%
9.9 6
 
0.1%
9.8 16
 
0.3%
9.7 19
 
0.4%
9.6 42
 
0.9%
9.5 64
1.3%
9.4 73
1.5%
9.3 113
2.3%
9.2 137
2.8%
9.1 136
2.8%

Real Guest Service Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1964909
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:23.874373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q17.6
median8.4
Q39
95-th percentile9.9
Maximum10
Range8
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.2152006
Coefficient of variation (CV)0.14825864
Kurtosis3.877246
Mean8.1964909
Median Absolute Deviation (MAD)0.7
Skewness-1.4407859
Sum39474.3
Variance1.4767125
MonotonicityNot monotonic
2023-06-15T19:28:24.073839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 234
 
4.9%
8 230
 
4.8%
8.9 208
 
4.3%
8.7 207
 
4.3%
9 205
 
4.3%
8.8 195
 
4.0%
8.3 189
 
3.9%
8.6 185
 
3.8%
8.5 182
 
3.8%
9.2 171
 
3.6%
Other values (58) 2810
58.3%
ValueCountFrequency (%)
2 15
0.3%
2.5 14
0.3%
2.7 3
 
0.1%
3 1
 
< 0.1%
3.3 2
 
< 0.1%
3.5 3
 
0.1%
3.8 4
 
0.1%
4 26
0.5%
4.1 1
 
< 0.1%
4.2 1
 
< 0.1%
ValueCountFrequency (%)
10 234
4.9%
9.9 15
 
0.3%
9.8 51
 
1.1%
9.7 57
 
1.2%
9.6 77
 
1.6%
9.5 86
 
1.8%
9.4 110
2.3%
9.3 162
3.4%
9.2 171
3.6%
9.1 146
3.0%

Real Guest Value for money Score
Real number (ℝ)

Distinct68
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3
Minimum2
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size37.8 KiB
2023-06-15T19:28:24.368053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6.3
Q17.8
median8.5
Q39
95-th percentile9.9
Maximum10
Range8
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.1302836
Coefficient of variation (CV)0.13617875
Kurtosis6.2142419
Mean8.3
Median Absolute Deviation (MAD)0.6
Skewness-1.7895303
Sum39972.8
Variance1.277541
MonotonicityNot monotonic
2023-06-15T19:28:24.650296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8 292
 
6.1%
9 236
 
4.9%
8.9 225
 
4.7%
8.6 219
 
4.5%
10 214
 
4.4%
8.4 209
 
4.3%
8.5 207
 
4.3%
8 203
 
4.2%
8.3 196
 
4.1%
8.7 195
 
4.0%
Other values (58) 2620
54.4%
ValueCountFrequency (%)
2 18
0.4%
2.1 2
 
< 0.1%
2.5 11
0.2%
3 1
 
< 0.1%
3.2 1
 
< 0.1%
3.3 3
 
0.1%
3.5 1
 
< 0.1%
3.8 3
 
0.1%
3.9 1
 
< 0.1%
4 18
0.4%
ValueCountFrequency (%)
10 214
4.4%
9.9 35
 
0.7%
9.8 52
 
1.1%
9.7 48
 
1.0%
9.6 73
 
1.5%
9.5 78
 
1.6%
9.4 103
2.1%
9.3 115
2.4%
9.2 178
3.7%
9.1 173
3.6%

Origin
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size37.8 KiB
Bangkok
2476 
Phuket
2100 
Ko Pha-ngan
 
114
Ko Phi Phi
 
111
Koh Samui
 
15

Length

Max length11
Median length7
Mean length6.7340116
Min length6

Characters and Unicode

Total characters32431
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBangkok
2nd rowBangkok
3rd rowBangkok
4th rowBangkok
5th rowBangkok

Common Values

ValueCountFrequency (%)
Bangkok 2476
51.4%
Phuket 2100
43.6%
Ko Pha-ngan 114
 
2.4%
Ko Phi Phi 111
 
2.3%
Koh Samui 15
 
0.3%

Length

2023-06-15T19:28:24.868711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-15T19:28:25.042413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
bangkok 2476
47.9%
phuket 2100
40.6%
ko 225
 
4.4%
phi 222
 
4.3%
pha-ngan 114
 
2.2%
koh 15
 
0.3%
samui 15
 
0.3%

Most occurring characters

ValueCountFrequency (%)
k 7052
21.7%
a 2719
 
8.4%
o 2716
 
8.4%
n 2704
 
8.3%
g 2590
 
8.0%
B 2476
 
7.6%
h 2451
 
7.6%
P 2436
 
7.5%
u 2115
 
6.5%
e 2100
 
6.5%
Other values (7) 3072
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26799
82.6%
Uppercase Letter 5167
 
15.9%
Space Separator 351
 
1.1%
Dash Punctuation 114
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
k 7052
26.3%
a 2719
 
10.1%
o 2716
 
10.1%
n 2704
 
10.1%
g 2590
 
9.7%
h 2451
 
9.1%
u 2115
 
7.9%
e 2100
 
7.8%
t 2100
 
7.8%
i 237
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
B 2476
47.9%
P 2436
47.1%
K 240
 
4.6%
S 15
 
0.3%
Space Separator
ValueCountFrequency (%)
351
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31966
98.6%
Common 465
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
k 7052
22.1%
a 2719
 
8.5%
o 2716
 
8.5%
n 2704
 
8.5%
g 2590
 
8.1%
B 2476
 
7.7%
h 2451
 
7.7%
P 2436
 
7.6%
u 2115
 
6.6%
e 2100
 
6.6%
Other values (5) 2607
 
8.2%
Common
ValueCountFrequency (%)
351
75.5%
- 114
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
k 7052
21.7%
a 2719
 
8.4%
o 2716
 
8.4%
n 2704
 
8.3%
g 2590
 
8.0%
B 2476
 
7.6%
h 2451
 
7.6%
P 2436
 
7.5%
u 2115
 
6.5%
e 2100
 
6.5%
Other values (7) 3072
9.5%

Interactions

2023-06-15T19:28:05.003711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:45.514844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:47.487024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:49.453109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:51.449227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:53.261791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:55.636825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:57.554024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:00.234519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:02.524203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:05.178246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:45.718300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:47.678512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:49.633627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:51.628395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:53.432335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:55.818339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:57.849234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:00.437529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:02.733643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:05.345797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:45.868898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:47.853955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:49.810664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:51.802929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:53.606868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:56.006835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:58.066172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:00.631525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:02.933110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:05.532297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:46.041435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:48.050429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:49.988186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:51.991425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:53.781402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:56.200317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:58.266062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:00.929018image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:03.162497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:05.732862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:46.204997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:48.224964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:50.155748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:52.163963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:53.989357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:56.386819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:58.466185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:01.193310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:03.403366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:05.934324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:46.380247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:48.405481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:50.403085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:52.344501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:54.285624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:56.577309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:58.726909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:01.402750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:03.640280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:06.200611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:46.565909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:48.716649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:50.667377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:52.520032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:54.521988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:56.788368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:59.108946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:01.603215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:03.871556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:06.503315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:46.810811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:48.890182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:50.872769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:52.690151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:54.732425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:56.983548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:59.394183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:01.791709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:04.120944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:06.908665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:47.080602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:49.082668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:51.062261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:52.877309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:54.984237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:57.174040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:59.680416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:01.986770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:04.351458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:07.463696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:47.297023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:49.268387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:51.252752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:53.068311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:55.236210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:57.367522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:27:59.968234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:02.227968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-15T19:28:04.595804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-15T19:28:25.217941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
PriceStarsScoreReviewsBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubOrigin
Price1.0000.3470.2650.0910.0310.2570.2940.1850.2340.2040.2120.0000.1110.0590.0940.0280.1640.0310.1790.1720.1940.0670.1020.2300.124
Stars0.3471.0000.0630.3920.2600.1110.156-0.0250.0970.0220.2470.0710.1470.3320.2820.3640.2540.1360.2910.3070.3090.4280.2130.3640.089
Score0.2650.0631.000-0.066-0.0330.8950.8760.6410.8410.8640.7490.0000.0940.1570.2200.2320.1040.1250.0940.1760.1720.2750.0890.1200.095
Reviews0.0910.392-0.0661.0000.568-0.055-0.0420.063-0.089-0.1030.0530.0000.1770.1050.0880.1160.0840.0000.0550.0830.1000.0660.0220.0870.049
Booked today0.0310.260-0.0330.5681.000-0.0070.0110.069-0.032-0.0500.0170.0330.1340.1440.0210.1540.0380.0270.0580.0440.0830.0970.0000.0770.100
Real Guest Cleanlines Score0.2570.1110.895-0.055-0.0071.0000.8960.5530.8580.8660.7610.0070.1020.1300.2300.1820.0920.1300.0810.1360.1710.2250.0790.1010.101
Real Guest Facilities Score0.2940.1560.876-0.0420.0110.8961.0000.5270.8280.8490.7330.0000.0820.1250.1840.1950.0940.1370.1250.1570.1780.2730.0790.1460.094
Real Guest Location Score0.185-0.0250.6410.0630.0690.5530.5271.0000.5350.5690.4210.0290.0590.1480.1570.1770.0900.1260.0570.1440.1620.2380.0700.0760.051
Real Guest Service Score0.2340.0970.841-0.089-0.0320.8580.8280.5351.0000.8090.6710.0330.0810.1150.2090.1780.0920.1240.0590.1410.1460.2260.0710.1020.108
Real Guest Value for money Score0.2040.0220.864-0.103-0.0500.8660.8490.5690.8091.0000.6400.0000.1030.1580.2070.2360.1020.1340.0780.1700.1700.3010.0770.1120.088
Sparkling clean0.2120.2470.7490.0530.0170.7610.7330.4210.6710.6401.0000.0210.1140.0640.1720.1310.0000.0000.0000.0360.0000.1160.0000.0130.099
NewlyBuilt0.0000.0710.0000.0000.0330.0070.0000.0290.0330.0000.0211.0000.0250.0430.0260.0060.0280.0280.0000.0000.0280.0290.0000.0070.000
ExcellentView0.1110.1470.0940.1770.1340.1020.0820.0590.0810.1030.1140.0251.0000.0150.0820.0560.0270.0000.0700.1380.1060.0800.0220.1070.265
Check In 24/70.0590.3320.1570.1050.1440.1300.1250.1480.1150.1580.0640.0430.0151.0000.1890.4440.1240.0760.0990.1890.1910.2410.1260.1600.149
AirportTransfer0.0940.2820.2200.0880.0210.2300.1840.1570.2090.2070.1720.0260.0820.1891.0000.2020.1800.0960.1410.2820.2640.2470.2060.1810.291
Front Desk0.0280.3640.2320.1160.1540.1820.1950.1770.1780.2360.1310.0060.0560.4440.2021.0000.1510.1060.0770.2570.2260.3550.1060.1620.176
Valet Parking0.1640.2540.1040.0840.0380.0920.0940.0900.0920.1020.0000.0280.0270.1240.1800.1511.0000.0050.0440.1840.1540.1390.1950.1620.088
Free WiFi In All Rooms0.0310.1360.1250.0000.0270.1300.1370.1260.1240.1340.0000.0280.0000.0760.0960.1060.0051.0000.0320.0770.0850.2590.0370.0000.146
Swimming Pool0.1790.2910.0940.0550.0580.0810.1250.0570.0590.0780.0000.0000.0700.0990.1410.0770.0440.0321.0000.1080.1080.0620.1600.0000.180
Bar0.1720.3070.1760.0830.0440.1360.1570.1440.1410.1700.0360.0000.1380.1890.2820.2570.1840.0770.1081.0000.4250.2610.1700.2190.184
Coffee0.1940.3090.1720.1000.0830.1710.1780.1620.1460.1700.0000.0280.1060.1910.2640.2260.1540.0850.1080.4251.0000.2310.1420.1680.083
DailyHousekeeping0.0670.4280.2750.0660.0970.2250.2730.2380.2260.3010.1160.0290.0800.2410.2470.3550.1390.2590.0620.2610.2311.0000.1290.1040.055
Golf0.1020.2130.0890.0220.0000.0790.0790.0700.0710.0770.0000.0000.0220.1260.2060.1060.1950.0370.1600.1700.1420.1291.0000.1570.162
Kids club0.2300.3640.1200.0870.0770.1010.1460.0760.1020.1120.0130.0070.1070.1600.1810.1620.1620.0000.0000.2190.1680.1040.1571.0000.160
Origin0.1240.0890.0950.0490.1000.1010.0940.0510.1080.0880.0990.0000.2650.1490.2910.1760.0880.1460.1800.1840.0830.0550.1620.1601.000

Missing values

2023-06-15T19:28:08.178024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-15T19:28:10.019806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NamePriceStarsScoreSpokenLanguagesReviewsLocationSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreOrigin
0Asia Hotel Bangkok (SHA Plus+)1194.07.9English, Thai27771296 Phayathai Road, Siam, Bangkok, Thailand, 1040000110101111100176.07.67.59.17.57.6Bangkok
1Rembrandt Hotel & Suites (SHA Plus+)1444.58.3English, Thai600119 Sukhumvit Soi 18, Klong Toei, Sukhumvit, Bangkok, Thailand, 101100011111101110032.08.58.18.48.38.6Bangkok
2Dream Hotel Bangkok (SHA Plus+)604.58.4English, Thai1610910 Sukhumvit Soi 15, Sukhumvit, Bangkok, Thailand, 101100001111111111031.08.88.18.58.48.7Bangkok
3VIX Bangkok @ Victory Monument693.09.2English, Chinese [Mandarin], Thai143913-15 Thanon Ratchawithi, Chatuchak, Bangkok, Thailand, 104001000010100110024.09.39.09.59.49.3Bangkok
4The Berkeley Hotel Pratunam (SHA Plus+)2385.08.2English, Chinese [Mandarin], Thai61617559 Ratchathewi, Pratunam, Bangkok, Thailand, 1040000011101011100128.08.38.18.88.28.1Bangkok
5Ambassador Hotel Bangkok (SHA Plus+)1204.07.2English, Thai22016171 Sukhumvit Rd., Soi 11, Wattana, Sukhumvit, Bangkok, Thailand, 101100011010111110099.07.06.98.46.86.9Bangkok
6Grand President Bangkok (SHA Plus+)1334.07.1English, Chinese [Mandarin], Thai1202716 Sukhumvit Soi 11 , Sukhumvit, Bangkok, Thailand, 101100001111111110018.06.86.78.37.17.0Bangkok
7Grand 5 Hotel & Plaza Sukhumvit (SHA Extra Plus)2574.07.9English, Thai300487 Sukhumvit Road Soi 5, Klongtoey Nua, Wattana, Bangkok, Sukhumvit, Bangkok, Thailand, 101100001010101110025.08.17.38.67.97.6Bangkok
8Mövenpick Hotel Sukhumvit 15 Bangkok355.08.3English, Thai3768Soi Sukhumvit 15, Sukhumvit, Bangkok, Thailand, 101100001111111110034.08.78.27.98.48.6Bangkok
9Asia Hotel Bangkok (SHA Plus+)1254.07.9English, Thai27771296 Phayathai Road, Siam, Bangkok, Thailand, 1040000110101111100176.07.67.59.17.57.6Bangkok
NamePriceStarsScoreSpokenLanguagesReviewsLocationSparkling cleanNewlyBuiltExcellentViewCheck In 24/7AirportTransferFront DeskValet ParkingFree WiFi In All RoomsSwimming PoolBarCoffeeDailyHousekeepingGolfKids clubBooked todayReal Guest Cleanlines ScoreReal Guest Facilities ScoreReal Guest Location ScoreReal Guest Service ScoreReal Guest Value for money ScoreOrigin
4806Villa Paradiso765.07.9English, Thai1Villa E, Malaiwana Estate,28/12 Moo 4, Tambon Sakoo, Amphur Thalang, Naithon, Phuket, Thailand, 83110000011010001100.05.07.57.57.510.0Phuket
4807Montana Hotel & Hostel Phuket703.09.6English, Thai1528/27 Patak Road, Karon, Phuket 83100, Karon, Phuket, Thailand, 83100100011010001000.010.07.510.010.010.0Phuket
4808Chomdao@Maikhao2974.07.5English9Rural Road Phuket 3033, Mai Khao, Phuket, Thailand, 83110000010010000000.06.06.02.06.02.0Phuket
4809Sealord Naithon Beachfront Villa1160.08.9English, Thai3131/10 Naithon Beach Road, Naithon, Phuket, Thailand, 83110100001010111000.09.59.29.39.48.7Phuket
4810Living Room Guesthouse & Cafe Bar3141.09.4English, Thai44516/6 Soi Centara, Muang, Phuket, Karon, Phuket, Thailand, 83100100000010111000.09.69.09.79.19.7Phuket
4811The Beach by Glitter House993.09.5English, Thai40110/54 Kata Road, Kata, Phuket, Thailand, 83100100010011011000.09.49.69.49.59.4Phuket
4812Village House CAC123632.09.6English, French, Thai6158/12 Soi Bang Thao 7, Moo5, Bangtao, Surin, Phuket, Thailand, 83110100010010001000.09.79.79.010.09.7Phuket
4813The Beach by Glitter House373.09.5English, Thai40110/54 Kata Road, Kata, Phuket, Thailand, 83100100010011011000.09.49.69.49.59.4Phuket
4814Westkey Kamala villa8435.09.5English, Thai3Kamala, Phuket, Thailand100101001010000.09.39.39.310.09.3Phuket
4815Bcollection Resort1985.09.6English, Thai1Layan, Phuket, Thailand100000001010000.010.08.010.010.010.0Phuket